Arterial spin labelling (ASL) magnetic resonance imaging (MRI) is an entirely non-invasive means to measure blood flow in the body, for example as the supply of blood to the tissues (perfusion) or to visualize blood flow within arteries (angiography). In ASL an endogenous ‘contrast agent’ is generated by radio-frequency inversion of the magnetization of flowing blood upstream from the organ being investigated, followed by subsequent imaging of this labelled blood once it has reached the organ. An example is labelling blood in the neck to measure blood flow in the brain. This image is subtracted from another taken in the absence of labelling to remove the static tissue signal and reveal the blood supply, from which quantitative measures of blood flow can be derived (Alsop and Detre, 1996; Buxton et al., 1998). The ASL contrast mechanism can also be applied to angiographic imaging of the vessels, including dynamic (‘cine’) acquisitions (Edelman et al., 1994; Wang et al., 1991).
Some organs, most notably the brain, are supplied with blood by a number of arteries, each artery supplying a different region; commonly referred to as vascular territories. It is of clinical relevance to be able to visualise the blood flow within individual arteries and the territories they supply. A notable example is collateral flow in the ‘Circle of Willis’ whereby blood may pass from one major artery via a communicating artery to feed a different vascular territory. For example, flow from an internal carotid artery may be diverted to a posterior territory normally supplied by the vertebral arteries in the case of vertebral occlusion or stenosis. A number of selective ASL labelling methods have been proposed that can target individual arteries (Helle et al., 2010a; Helle et al., 2010b) (Dai et al., 2010) (Davies and Jezzard, 2003) (Zimine et al., 2006) (Hendrikse et al., 2004).
Recently a more efficient strategy has been demonstrated for the simultaneous labelling of multiple arteries (Gunther, 2006) (Wong, 2007), termed vessel-encoded ASL (VE-ASL). This method can be used to produce vessel-selective dynamic angiograms of the major cerebral arteries (Okell et al., 2010). Vessel encoded ASL extends the ASL concept to the unique labelling of the arteries so their individual contributions can be visualized and quantified. This is achieved by modulation of the labelling profile over a series of image acquisitions. The blood in the different arteries ends up being uniquely encoded in the data. We might say it has been barcoded. The method is relatively efficient, since in every cycle of the acquisition multiple arteries are labelled (albeit to differing degrees). This is in contrast to methods where individual arteries are targeted one at a time.
An example of VE-ASL imaging is given in FIG. 1. Conventional ASL fully inverts the blood in all arteries within the labelling regions. Subtraction of the subsequent image from a control, in the absence of labelling, produces an image of flowing blood. VE-ASL spatially modulates the inversion process such that in one acquisition within a subset of arteries the blood will be inverted and the remainder will remain in the unlabelled (control) condition. Over a number of such acquisitions, with different modulations, each artery will have been uniquely encoded and its contribution to the blood flow image can be extracted in post-processing. Complications arise, however, because it is necessary to use post-processing to separate the different artery contributions.
The most straightforward approach to post-processing such data involves simple addition or subtraction of images (typically written as the equivalent matrix operation). However, more complex encodings and imperfections in the modulation, for example due to non-ideal locations of the arteries within the labelling region, necessitate more careful analysis (Chappell et al., 2010; Wong et al., 2006). This typically involves the specification of the mixing or encoding matrix (FIG. 1) for the different artery contributions under the encoding cycles employed in the acquisition. The (Moore-Penrose) pseudo-inverse of this matrix can then be used in post-processing to calculate individual artery contributions to the flow.
We previously proposed a general framework for the analysis of VE-ASL data employing a Bayesian solution (Chappell et al., 2010). It offered a number of advantages over existing matrix inversion approaches. In particular, it provided a full model for the relationship between the locations of the arteries in the labelling plane and the encoding profile, allowing the locations to be inferred from the data whilst permitting prior information from planning acquisitions to be incorporated. Furthermore, a voxel-wise classification with N-arteries per class was used to restrict the number of arteries that were assumed to contribute to the signal in a voxel, resulting in greater signal-to-noise ratio (SNR) efficiency and reductions in the number of encoding cycles required.
The general framework was demonstrated on VE-ASL imaging of the cerebral vascular territories (Chappell et al., 2010). However, a number of limitations were present in the existing framework that become more acute in the case of angiographic data, particularly when coupled with clinical application. First, the original proposal employs a Markov Chain Monte Carlo (MCMC) sampling procedure to infer the ‘global’ parameters, e.g. the artery locations in the labelling region or region of interest (ROI). This results in a relatively long computation time, something that will be more pronounced in higher resolution angiographic data. Second, while there are potential benefits in fully inferring the artery locations from the data, in a clinical setting it may be more beneficial to constrain the artery locations based on information obtained in the planning phase and according to variation that might be expected due to patient movement, for example, constraining the artery locations by only permitting a global three degree-of-freedom transformation. This will result in fewer parameters in the analysis and so should be more robust to the poorer signal-to-noise ratio (SNR) and greater frequency of motion artefacts expected of clinical data, which is caused by greater decay of the ASL label during the delayed blood transit, typical of patients with cerebrovascular disease. Additionally, in patients with highly stenosed arteries that provide little signal downstream, allowing completely free determination of the artery locations leaves the analysis vulnerable to bias by artefacts and incorrect assignment of the signal components. In these cases it may sometimes be critical to differentiate between low flow and zero flow, so a method that is robust to small signals from some arteries is desirable.
Accordingly, there is a need to address the aforementioned deficiencies and inadequacies.